```
import pandas as pd
import json

with open("historical") as f:
    json_data = json.load(f)

data_dict = {d["date"]: d for d in json_data}

df = pd.DataFrame(data_dict.values())
df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
df.set_index("date", inplace=True)

min_date = df.index.min()
max_date = df.index.max()
all_dates = pd.date_range(start=min_date, end=max_date, freq="D")
all_dates_df = pd.DataFrame(index=all_dates)

merged_df = pd.merge(all_dates_df, df, how="left", left_index=True, right_index=True)

missing_dates = merged_df[merged_df.isnull().any(axis=1)].index.strftime("%Y-%m-%d").tolist()
print(f"Missing dates: {missing_dates}")

```

# Importing necessary libraries
import pandas as pd    # Pandas for data manipulation
import json             # JSON for reading JSON file

# Read the JSON data from a local file
with open("historical") as f:
    json_data = json.load(f)   # Reading data from file

# Convert the list of dictionaries to a dictionary
data_dict = {d["date"]: d for d in json_data}  # Creating a dictionary with date as key and dictionary as value

# Load the dictionary into a Pandas DataFrame
df = pd.DataFrame(data_dict.values())  # Converting dictionary values to DataFrame
df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")  # Converting date column to datetime format
df.set_index("date", inplace=True)  # Setting index to date column

# Create a new DataFrame with all dates between the earliest and latest dates in the original DataFrame
min_date = df.index.min()  # Getting minimum date from index
max_date = df.index.max()  # Getting maximum date from index
all_dates = pd.date_range(start=min_date, end=max_date, freq="D")  # Creating range of all dates
all_dates_df = pd.DataFrame(index=all_dates)  # Creating DataFrame with all dates as index

# Merge the original DataFrame with the new DataFrame containing all dates to find any missing dates
merged_df = pd.merge(all_dates_df, df, how="left", left_index=True, right_index=True)  # Merging DataFrames to get missing dates

# Print any missing dates in the dataset
missing_dates = merged_df[merged_df.isnull().any(axis=1)].index.strftime("%Y-%m-%d").tolist()  # Filtering missing dates from merged DataFrame
print(f"Missing dates: {missing_dates}")  # Printing list of missing dates

The code imports necessary libraries - Pandas and JSON, reads JSON data from a local file, creates a dictionary with date as key and dictionary as value, converts the dictionary to a Pandas DataFrame, sets the index of the DataFrame to the date column, creates a new DataFrame with all dates between the earliest and latest dates in the original DataFrame, merges the original DataFrame with the new DataFrame containing all dates to find any missing dates, filters missing dates from the merged DataFrame, and finally prints a list of the missing dates.